# Evaluating the Quality of Finite Element Meshes with Machine Learning

arXiv: https://arxiv.org/abs/2107.10507 · HTML: https://mlsanity.com/paper/2107.10507/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 19 references that are in the map.
- First posted on arXiv 22 Jul 2021.

## Builds on (most cited first, 4 of 4)

- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Gated Graph Sequence Neural Networks](https://mlsanity.com/paper/1511.05493/) (2015)
- [Learning Visual Commonsense for Robust Scene Graph Generation](https://mlsanity.com/paper/2006.09623/) (2020)
- [Network Data](https://mlsanity.com/paper/1912.06346/) (2019)

## Built on by (most cited first, 3 of 3)

- [Learning robust marking policies for adaptive mesh refinement](https://mlsanity.com/paper/2207.06339/) (2022)
- [A Survey of AI Methods for Geometry Preparation and Mesh Generation in Engineering Simulation](https://mlsanity.com/paper/2512.23719/) (2025)
- [MQENet: A Mesh Quality Evaluation Neural Network Based on Dynamic Graph Attention](https://mlsanity.com/paper/2309.01067/) (2023)

## Abstract (arXiv)

This paper addresses the problem of evaluating the quality of finite element meshes for the purpose of structural mechanic simulations. It proposes the application of a machine learning model trained on data collected from expert evaluations. The task is characterised as a classification problem, where quality of each individual element in a mesh is determined by its own properties and adjacency structures. A domain-specific, yet simple representation is proposed such that off-the-shelf machine learning methods can be applied. Experimental data from industry practice demonstrates promising results.